How will the Process
from adopting AI?

Artificial intelligence proposes a radically different approach to the traditional scientific method. It wants to automate the learning processes of that experienced operator.


Because although humans are smarter, computers are faster. And luckily humans are smart enough to make computers mimic the human learning process.


These are AI algorithms.


plants and enterprises

Traditionally, the road from discovery to production starts with scientists developing theories about causal relations and the use of mathematics to describe these cause-and-effects. Then after a series of experiments at increasing scales, engineers apply the science to design and operate processes at large industrial plants.

Having the need to know the underlying mechanics in order to develop better mathematical models is slowing the whole process down significantly. Much more valuable is that experience-based expert operator’s knowledge… Isn’t it?

Talk to your Faktion AI Consultant to learn how we can accelerate this whole process and what the benefits are.

An experience-based expert
operator's knowledge
is not enough

They accumulated the knowledge
over years-to-decades of experience.

They are not always available,
only during their shifts.

They cannot know what has never occurred in operations or experiments.

An ai toolbox
as shortcut

to classical mathematical models

ArtificiaI Intelligence looks at the experiments and
measurements and derives the model from that data
That’s why many AI algorithms are called ‘learning’
algorithms. They learn the solution without any prior paradigm, just from the data.


Technically, it is important to understand that this happens very much like human experienced-based learning: by repeated trial and error. AI algorithms provide the procedure to try something out, learn from it, adapt the model intelligently for the next time and then try it again.

Classic method


Artificial Intelligence

Limited complexity

Very general and broad

Scientific principles

Fast training, development

Very specific and precise

Based on correlations

Business improvement

should always be the goal

The potential for business value creation is especially large in the process industry, given the complexity and high value of the industry.

Today, processing plants have already made substantial
investments in process monitoring with mostly traditional and some AI-powered sensor tech. These sensors ‘measure’ the process and the resulting state of the process.


However, operators must still rely on their experience, intuition, and judgment for process analysis and control. They are expected to monitor a multitude of information and adjust the process settings as required.

At the same time, they must troubleshoot and run tests and trials. Thus, many operators take shortcuts and prioritize urgent activities that don’t necessarily add value.

What are the drivers behind an
AI implementation in this industry?

Industry under
environmental pressure


Data readiness
of the process industry


Many continuous processes require
fast adaptions

Marginal improvements yield
large bottom-line results


Completely automated
process control

Mathematical knowledge
of process managers


Scarce & expensive
labor force with expertise


Sensors to detect anomalies
humans can’t detect

there is so much more

and we are here to help

We are aware that the value for companies is not in the theory, but in seeing potential applications.


For the implementation, everything is there, expert AI service companies exist, and the basic theory is mature enough to be implemented in a controlled and targeted fashion.


At the same time, the AI framework is disruptive enough for the results to be not less than breakthroughs.

Cherry-pick the AI solutions that
deliver the most value to your business

Business Value
When the problem is solved and implemented, the solution will yield significant and recurring value. As explained in the second chapter, the goal is always business value and may never be technological advancement as such.
The problem defined is recognized as a priority across the business, by all stakeholders at business and plant level. Resources are made available to solve the problem.

Data Availability & Quality
It should be clear by now that, an AI solution starts from the data. Proper quality & availability of data is determined by: Data accuracy, consistency, timeliness, completeness & frequency.

Problem Complexity
Is the problem complex enough that traditional methods cannot solve it? At Faktion, we don’t want to make things more complex than needed. Hence, conventional methods should first be deemed insufficient.

Implementation Potential
Once solved, the solution can be implemented to deliver the impact with low CAPEX, on a short time frame and on a large scale.

AI solutions

for process industry

Technological improvements are merely a means to an end. It sounds obvious but it’s often confused. The potential for business value creation is especially large in the process industry, given the complexity and high value of the industry.​

Get your introduction to the benefits
of AI in your business today.


An extended whitepaper will be available soon.

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AI in the Process Industry